# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.

from typing import Dict, Optional
import torch
from torch import nn, Tensor
from torch.nn import Parameter
import torch.nn.functional as F

from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state


@with_incremental_state
class MultiheadAttention(nn.Module):
    """Multi-headed attention.
    See "Attention Is All You Need" for more details.
    """
    def __init__(self, 
                 embed_dim,
                 num_heads,
                 kdim=None,
                 vdim=None,
                 dropout=0.,
                 bias=True, 
                 add_bias_kv=False, 
                 add_zero_attn=False,
                 self_attention=False,
                 encoder_decoder_attention=False,
                ):
        super().__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
        self.inner_dim = embed_dim

        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = self.inner_dim // num_heads
        assert self.head_dim * num_heads == self.inner_dim, "inner_dim must be divisible by num_heads"
        self.scaling = self.head_dim ** -0.5

        self.self_attention = self_attention
        self.encoder_decoder_attention = encoder_decoder_attention

        assert not self.self_attention or self.qkv_same_dim, (
            "Self-attention requires query, key and " "value to be of the same size"
        )

        # Param (out, in)
        if self.qkv_same_dim:
            self.in_proj_weight = Parameter(torch.Tensor(3 * self.inner_dim, self.embed_dim))
        else:
            assert self.kdim == self.vdim, "In this version kdim must be equal to vdim"
            self.kv_proj_weight = Parameter(torch.Tensor(2 * self.inner_dim, self.kdim))
            self.q_proj_weight = Parameter(torch.Tensor(self.inner_dim, self.embed_dim))

        if bias:
            self.in_proj_bias = Parameter(torch.Tensor(3 * self.inner_dim))
        else:
            self.register_parameter('in_proj_bias', None)

        self.out_proj = nn.Linear(self.inner_dim, self.embed_dim, bias=bias)

        if add_bias_kv:
            self.bias_k = Parameter(torch.Tensor(1, 1, self.inner_dim))
            self.bias_v = Parameter(torch.Tensor(1, 1, self.inner_dim))
        else:
            self.bias_k = self.bias_v = None

        self.add_zero_attn = add_zero_attn

        self.reset_parameters()

        self.onnx_trace = False

    def prepare_for_onnx_export_(self):
        self.onnx_trace = True

    def reset_parameters(self):
        if self.qkv_same_dim:
            nn.init.xavier_uniform_(self.in_proj_weight)
        else:
            nn.init.xavier_uniform_(self.kv_proj_weight)
            nn.init.xavier_uniform_(self.q_proj_weight)

        nn.init.xavier_uniform_(self.out_proj.weight)
        if self.in_proj_bias is not None:
            nn.init.constant_(self.in_proj_bias, 0.)
            nn.init.constant_(self.out_proj.bias, 0.)
        if self.bias_k is not None:
            nn.init.xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            nn.init.xavier_normal_(self.bias_v)

    def forward(self, query, key, value, key_padding_mask=None,
                incremental_state=None, need_weights=True, 
                static_kv=False, attn_mask=None, **kwargs):
        """Input shape: Time x Batch x Channel

        Self-attention can be implemented by passing in the same arguments for
        query, key and value. Timesteps can be masked by supplying a T x T mask in the
        `attn_mask` argument. Padding elements can be excluded from
        the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
        batch x src_len, where padding elements are indicated by 1s.

        """

        qkv_same = query.data_ptr() == key.data_ptr() == value.data_ptr()
        kv_same = key.data_ptr() == value.data_ptr()
        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]
        assert key.size() == value.size()

        if incremental_state is not None:
            saved_state = self._get_input_buffer(incremental_state)
            if 'prev_key' in saved_state:
                # previous time steps are cached - no need to recompute
                # key and value if they are static
                if static_kv:
                    assert kv_same and not qkv_same
                    key = value = None
        else:
            saved_state = None

        if qkv_same:
            # self-attention
            q, k, v = self.in_proj_qkv(query)
        elif kv_same:
            # encoder-decoder attention
            q = self.in_proj_q(query)
            if key is None:
                assert value is None
                k = v = None
            else:
                k, v = self.in_proj_kv(key)
        else:
            q = self.in_proj_q(query)
            k = self.in_proj_k(key)
            v = self.in_proj_v(value)
        q *= self.scaling

        if self.bias_k is not None:
            assert self.bias_v is not None
            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
            if attn_mask is not None:
                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
            if key_padding_mask is not None:
                key_padding_mask = torch.cat(
                    [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)

        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
        if k is not None:
            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
        if v is not None:
            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)

        if saved_state is not None:
            # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
            if 'prev_key' in saved_state:
                prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim)
                if static_kv:
                    k = prev_key
                else:
                    k = torch.cat((prev_key, k), dim=1)
            if 'prev_value' in saved_state:
                prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim)
                if static_kv:
                    v = prev_value
                else:
                    v = torch.cat((prev_value, v), dim=1)

            saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim)
            saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim)
            self._set_input_buffer(incremental_state, saved_state)

        src_len = k.size(1)

        if key_padding_mask is not None:
            assert key_padding_mask.size(0) == bsz, 'looking for bsz=%d, found %d in the mask' % (bsz, key_padding_mask.size(0))
            assert key_padding_mask.size(1) == src_len, 'looking for src_len=%d, found %d in the mask' % (src_len, key_padding_mask.size(1))

        if self.add_zero_attn:
            src_len += 1
            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
            if attn_mask is not None:
                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
            if key_padding_mask is not None:
                key_padding_mask = torch.cat(
                    [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)

        attn_weights = torch.bmm(q, k.transpose(1, 2))
        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]

        if attn_mask is not None:
            if attn_mask.dim() == 2: # same mask for all samples:
                attn_mask = attn_mask.unsqueeze(0)
                if self.onnx_trace:
                    attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
            elif attn_mask.dim() == 3:
                attn_mask = attn_mask.repeat_interleave(self.num_heads, dim=0)
            attn_weights += attn_mask

        if key_padding_mask is not None:
            # don't attend to padding []symbols
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            if self.onnx_trace:
                attn_weights = torch.where(
                    key_padding_mask.unsqueeze(1).unsqueeze(2),
                    torch.Tensor([float("-Inf")]),
                    attn_weights.float()
                ).type_as(attn_weights)
            else:
                attn_weights = attn_weights.float().masked_fill(
                    key_padding_mask.unsqueeze(1).unsqueeze(2),
                    float('-inf'),
                ).type_as(attn_weights)  # FP16 support: cast to float and back
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        attn_weights = utils.softmax(
            attn_weights, dim=-1, onnx_trace=self.onnx_trace,
        ).type_as(attn_weights)
        attn_weights = F.dropout(attn_weights, p=self.dropout, training=self.training)

        attn = torch.bmm(attn_weights, v)
        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
        if (self.onnx_trace and attn.size(1) == 1):
            # when ONNX tracing a single decoder step (sequence length == 1)
            # the transpose is a no-op copy before view, thus unnecessary
            attn = attn.contiguous().view(tgt_len, bsz, self.inner_dim)
        else:
            attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.inner_dim)
            attn = self.out_proj(attn)

            if need_weights:
                attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
                # average attention weights over heads
                attn_weights = attn_weights.sum(dim=1) / self.num_heads
                # The second head:
                # attn_weights = attn_weights[:, 1]
                # Keep the heads separate:
                # attn_weights = [attn_weights[:, h] for h in range(self.num_heads)]
            else:
                attn_weights = None

        return attn, attn_weights

    def in_proj_qkv(self, query):
        return self._in_proj(query).chunk(3, dim=-1)

    def in_proj_kv(self, key):
        if self.qkv_same_dim:
            return self._in_proj(key, start=self.inner_dim).chunk(2, dim=-1)
        else:
            bias = self.in_proj_bias
            if bias is not None:
                bias = bias[self.inner_dim:]
            return F.linear(key, self.kv_proj_weight, bias).chunk(2, dim=-1)

    def in_proj_q(self, query):
        if self.qkv_same_dim:
            return self._in_proj(query, end=self.inner_dim)
        else:
            bias = self.in_proj_bias
            if bias is not None:
                bias = bias[:self.inner_dim]
            return F.linear(query, self.q_proj_weight, bias)

    def in_proj_k(self, key):
        if self.qkv_same_dim:
            return self._in_proj(key, start=self.inner_dim, end=2 * self.inner_dim)
        else:
            weight = self.kv_proj_weight[:self.inner_dim, :]
            bias = self.in_proj_bias
            if bias is not None:
                bias = bias[self.inner_dim:2 * self.inner_dim]
            return F.linear(key, weight, bias)

    def in_proj_v(self, value):
        if self.qkv_same_dim:
            return self._in_proj(value, start=2 * self.inner_dim)
        else:
            weight = self.kv_proj_weight[self.inner_dim:, :]
            bias = self.in_proj_bias
            if bias is not None:
                bias = bias[2 * self.inner_dim:]
            return F.linear(value, weight, bias)

    def _in_proj(self, input, start=0, end=None):
        weight = self.in_proj_weight
        bias = self.in_proj_bias
        weight = weight[start:end, :]
        if bias is not None:
            bias = bias[start:end]
        return F.linear(input, weight, bias)

    @torch.jit.export
    def reorder_incremental_state(
        self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order: Tensor
    ):
        """Reorder buffered internal state (for incremental generation)."""
        input_buffer = self._get_input_buffer(incremental_state)
        if input_buffer is not None:
            for k in input_buffer.keys():
                input_buffer_k = input_buffer[k]
                if input_buffer_k is not None:
                    if self.encoder_decoder_attention and input_buffer_k.size(0) == new_order.size(0):
                        break
                    input_buffer[k] = input_buffer_k.index_select(0, new_order)
            incremental_state = self._set_input_buffer(incremental_state, input_buffer)
        return incremental_state

    def _get_input_buffer(
        self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
    ) -> Dict[str, Optional[Tensor]]:
        result = self.get_incremental_state(incremental_state, "attn_state")
        if result is not None:
            return result
        else:
            empty_result: Dict[str, Optional[Tensor]] = {}
            return empty_result

    def _set_input_buffer(
        self,
        incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
        buffer: Dict[str, Optional[Tensor]],
    ):
        return self.set_incremental_state(incremental_state, "attn_state", buffer)

    
